In the United States, lung cancer and colon cancer are the first and second leading cancer killers, respectively. Early detection of colonic polyps and lung nodules, the precursors to these diseases, has been shown to improve survival. Therefore, early detection of precancerous growths has become important so that they can be removed before evolving into a frank malignancy. In order to alert the radiologist to locations of possible nodules or polyps, a variety of methods have been proposed aimed at increasing the sensitivity or accuracy of detection (see, for instance, U.S. Pat. No. 4.907,156 to Doi et al.; U.S. Pat. No. 5,458,111 to Johnson et al.; U.S. Pat. Nos. 5,920,319 and 6,083,162 to Vining et al.; U.S. Pat. Nos. 5,971,767 and 6,331,116 to Kaufman et al., U.S. Pat. Nos. 6,088,473 and 6,141,437 to Xu et al. or U.S. Pat. No. 6,301,378 to Karssemeijer et al.). However, one of the limitations of detection methods focusing on increased sensitivity or accuracy of detection is that they could easily lead to a high false positive rate due to structures in the colon or lung with convex surfaces, such as haustral folds or pulmonary blood vessels. In other words, the increased sensitivity reduces the number of false negatives, but the increased sensitivity tends to increase the number of false positive detections (See, for instance, U.S. Pat. No. 5,289,374 to Doi et al., U.S. Pat. No. 5,657,362 to Giger et al.; U.S. Pat. No. 5,987,094 to Clarke et al. or U.S. Pat. No. 6,240,201 to Xu et al.). Furthermore, a detection due to false positive structures is usually based on the shape of the structure, which is often adjacent to other anatomical structures, thus making segmentation or elimination of false positive shapes difficult. For instance, a colonic polyp is always attached to the colon wall and some lung nodules are adjacent to either the chest wall or pulmonary vessels. However, automatically determining which portions of the image correspond to the shape of interest and which correspond to adjacent but distinct anatomical structures is very difficult.
Accordingly, there is a need to develop new methods to characterize shapes in medical images to determine which portions of the medical image correspond to a shape of interest. In particular, such a method for characterization of shapes is needed to provide accurate and early detection of pre-cancerous or cancerous growths.